Explore the synergy between wavelet transforms and machine learning for optimized feature extraction. Seeking insights on their combined impact in signal processing and pattern recognition.
The incorporation of wavelet transforms in machine learning applications enhances feature extraction by providing a powerful framework for capturing both temporal and frequency information at multiple scales. This synergy allows for more effective signal processing and pattern recognition across diverse domains.
The adaptation of wavelet transforms to the neural networks design used for classification enhances the feature extraction procedure by producing features invariant to different deformations of the images. In this respect, Stephan Mallat and Joan Bruna have conceived the scattering convolution network concept, see for example: Joan Bruna and Stephane Mallat, Invariant Scattering Convolution Networks, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 35, NO. 8, AUGUST 2013. A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. Recently, a Matlab toolbox, entitled Image scattering toolbox, was conceived.
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